{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.1 - K Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1.1. K-Means Clustering Example using Dummy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x215a958b790>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating dummy data of 500 records with 4 clusters\n",
    "features, labels = make_blobs(n_samples=500, centers=4, cluster_std = 2.00)\n",
    "\n",
    "#plotting the dummy data\n",
    "plt.scatter(features[:,0], features[:,1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performing kmeans clustering using KMeans class\n",
    "km_model = KMeans(n_clusters=4)\n",
    "km_model.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.54070231  7.26625699]\n",
      " [ 0.10118215 -0.23788283]\n",
      " [ 2.57107155  8.17934929]\n",
      " [-0.38501161  3.11446039]]\n"
     ]
    }
   ],
   "source": [
    "#printing centroid values\n",
    "print(km_model.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 3 2 1 1 3 1 2 0 0 2 3 3 1 1 2 0 1 2 2 1 3 3 1 1 0 2 0 2 0 1 0 1 3 2 2\n",
      " 3 0 0 0 2 1 2 0 1 3 1 3 2 1 3 3 1 0 2 1 3 0 0 3 3 3 1 1 1 3 0 1 3 2 1 1 2\n",
      " 0 2 1 2 1 0 0 2 1 2 1 0 2 0 0 2 2 3 3 0 2 0 2 3 0 0 3 1 0 3 2 1 3 2 2 0 2\n",
      " 1 1 0 0 3 3 2 3 1 0 0 3 0 1 0 3 1 0 3 2 0 1 1 0 2 1 2 2 0 3 1 3 3 0 1 1 0\n",
      " 2 0 0 0 3 3 3 3 0 3 1 2 1 0 3 2 3 1 3 3 0 3 2 3 0 1 3 2 3 2 1 2 2 3 0 3 2\n",
      " 0 3 0 1 2 2 3 2 2 1 0 1 1 2 3 2 0 1 3 3 3 3 0 0 3 1 0 1 1 3 3 1 3 1 0 0 2\n",
      " 1 1 1 1 2 2 0 2 1 0 1 2 3 0 1 2 0 1 1 0 1 0 3 1 2 1 1 2 3 0 0 1 3 1 2 0 1\n",
      " 1 0 1 0 0 2 2 0 1 2 0 1 2 0 0 1 1 0 1 2 3 0 1 2 3 0 0 3 2 3 0 3 1 3 1 3 0\n",
      " 1 3 3 1 1 2 2 2 3 1 1 3 1 3 3 0 1 1 2 0 2 2 3 1 0 3 2 1 0 2 3 1 0 2 0 0 3\n",
      " 1 1 2 3 3 1 2 2 3 0 3 3 3 1 0 2 0 0 3 1 1 0 1 0 3 1 3 1 0 0 1 3 1 2 0 0 0\n",
      " 1 1 0 0 2 0 0 2 2 3 2 3 3 3 0 3 1 1 1 1 3 1 1 1 2 3 0 2 3 3 1 1 3 3 3 3 3\n",
      " 0 0 3 2 0 3 2 1 1 3 2 1 2 1 1 1 3 3 2 3 1 1 1 2 0 2 1 1 0 0 3 1 2 3 0 2 0\n",
      " 2 0 2 3 3 2 2 0 0 2 0 0 0 1 3 2 2 1 1 2 1 1 0 1 2 1 0 0 2 2 0 3 3 0 0 2 1\n",
      " 3 2 0 3 3 1 2 1 1 3 0 3 3 0 0 1 2 3 1]\n"
     ]
    }
   ],
   "source": [
    "#printing predicted label values\n",
    "print(km_model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x215a9a7c6d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pring the data points\n",
    "plt.scatter(features[:,0], features[:,1], c= km_model.labels_, cmap='rainbow' )\n",
    "\n",
    "#print the centroids\n",
    "plt.scatter(km_model.cluster_centers_[:, 0], km_model.cluster_centers_[:, 1], s=100, c='black')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x215a9ae70a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print actual datapoints\n",
    "plt.scatter(features[:,0], features[:,1], c= labels, cmap='rainbow' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1.2. K-Means Clustering of Iris Plants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "iris_df = sns.load_dataset(\"iris\")\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dividing data into features and labels\n",
    "features = iris_df.drop([\"species\"], axis = 1)\n",
    "labels = iris_df.filter([\"species\"], axis = 1)\n",
    "features.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training KMeans model\n",
    "features = features.values\n",
    "km_model = KMeans(n_clusters=4)\n",
    "km_model.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 2 3 2 3 2 3 3 3 3 2 3 2 3 3 2 3 2 3 2 2\n",
      " 2 2 2 2 2 3 3 3 3 2 3 2 2 2 3 3 3 2 3 3 3 3 3 2 3 3 0 2 0 0 0 0 3 0 0 0 2\n",
      " 2 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 2 0 0 0 2 0 0 0 2 0 0 0 2 2\n",
      " 0 2]\n"
     ]
    }
   ],
   "source": [
    "print(km_model.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x215aa24b100>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pring the data points\n",
    "plt.scatter(features[:,0], features[:,1], c= km_model.labels_, cmap='rainbow' )\n",
    "\n",
    "#print the centroids\n",
    "plt.scatter(km_model.cluster_centers_[:, 0], km_model.cluster_centers_[:, 1], s=100, c='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training KMeans on K values from 1 to 10\n",
    "loss =[]\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(n_clusters = i).fit(features)\n",
    "    loss.append(km.inertia_)\n",
    "\n",
    "#printing loss against number of clusters\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1, 11), loss)\n",
    "plt.title('Finding Optimal Clusters via Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training KMeans with 3 clusters\n",
    "km_model = KMeans(n_clusters=3)\n",
    "km_model.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x215aa319940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xgE1x/wAfsg7giKCH4yPAVwkU9eVsJxgVYqmBklCZBUEQWh5b0S9KKadSaiWwH/hQa73I4rAJSqlVSqm5Sqlj44wzXSm1VCm1tKCgoAlmpzZ1xaKXUMlB4idmFGKSOcqpstxfTnXCZtGHKCMQZyzjjhEEoaWxVftFax0ARiqlOgJvKKWGaa2/CTtkOdBXa12qlJoCvAkMshjnSeBJMAulTbY+RUnHze7N2/nq4TdYNesTqksr8WSlM2LaaVx512D6D8jj2zjdh2oKb3UhiwIL8c8lM2E+9aPozHfsj/lEEERLNUdBSBINilPXWhcCnwLnRG0vrnHRaK3fA9xKqS6JMrK9kTV3H48N/ynLZn5AdUkFaE11SQXLZ85j5PCR7Jv7DekWz2MnikkMBuBsjo3pUOTCwdkckzA7R9AbL+6IUEw3DgbTjS5kJew8giDYp15RV0rlhWboKKUygDOBb6OO6a6UqZeplBobGvdg4s1NfTZv3sz/TL0ZX3kVQV/kDDjg81NeXs5lUy/jvM19yScXR2je3ZMO3MZpeEJi358uTGNcqKyvh7505keMYyBdE2arBxc3cgrHcxTZpNEJL6cxhB8yMmHnEAShYdhxv/QAnldKOTFiPVtrPUcpdTOA1vpxYCrwE6WUH6gArtDJCoBv4zz88MP4fL46j/H5fDz29xnMmDGjzuOOojPXYBWolDi8eJjMMCYzrFnPIwiCPST5KIpCypnPBrZwgHRcjKMfY+jbID/0BvYyh28oowonihH0ZgrDbDXbyMnJoaSkxNZxbxYtZhU7CaA5lh6cymAywjoXbeUgn7CBA5SSSyanMYR+NMwr9jEbWMj3+AmSjoszGcpojmrQGK2BoB++/jsseQx8ZTDkfDj9Acjq3oBBKipMavtzz4HfD5ddBr/9LXQKqwv06afwq1+ZErKDB8Mf/gCnn57YixHaNZJR2gBKqeSffE4lviMxHW6cDKc359qciW6mgJdYHLO9H7lcbWPW7HA4sPM3UQ7FHwLvHqno6ETRES83MxEnDjZRwGyWRlR8dOPgEkYzmG62rmUOq1keKgccznkc1+aE/dXL4Ls54A9VenW4wJsHt66HdDtrulrDqafCkiVQGYrs8XhMU4fVq83PH3wAF18cWU7W64XZs+HccxN+TUL7pD5Rl4JeYSxiK9VRQXo+AqxkB6U2Q/TeZY3l9i0cpJz6y4lmZdlbYPRkZUQIdgBNCZV8G0pamse6mBK+PoLMY72t8YMELQUd4EObY7QWDn4H371TK+hgZu5VRbDSbu2oL7+E5ctrBR1Medhdu+CNN8zvd94ZWx+8vNxsF4QWQkQ9jO0csowRd+Fgfx2x4eHUFZ+91cba8bRp03C4667R4nK7GHl17Ef6agLsDHVPihfLfogyW3HqNfHuVsSrO99a2bMCHO7Y7b5y2L7A5iDLlhmXSzSlpbAolLaxYYP1azduNDN9QWgBRNTDyCXL0nceIEgHMmyNkVbH2nM3G9Ue77rrLpzuutev3W43p/zs4tjtOOmMKWMar5epF4+t9YGcOnqhOhNYO6Yl6JiPZQ600wO5Q2wOkp9vXCzReL0wwPSWpWucyKK8PGmmLLQYIuphjKdfTGy3E0VvOpFLpq0xTg3FiUfTgQxybcRuDxgwgN+89v9we9NiZuxOtwuv18vs116l54DYxVsniuMwZUtPYUBMDXg3Tk4ONbeuDxcuetHRct8Y+toao7XQayx0GhA7W3d6YMzNNgeZMgU6dABn1Kcojwd+9CPz87331tYGr8HrhXvuaZTdgtAYRNTD6Eo2lzOGDmTgxIETB4PpzuUcb3uMseQznvwIuc0lk5s42fYY902+iX+sns3x088hLceLcijSczKZPv1GVq9ezXmTz+W/OJE+dMSBwomiOzlcx4mkh6JfxpDPRAbiwYUr1GTjZAYwjn627biWCfSKygw9hh6cjWUViFaLUnDNR9D/LCPkNTP0afOgg931Xrfb+NVPPNEIuccDo0bBggXQMfTwu/VW+PWvITsb0tPN93vvhdtvb7ZrE4RoJPrFAo2mnGrcOI8k8zSUIEEKKCWb9Li10OvDT5CDlNKBjCNiHY2J1NFx65gHCFJBNRl4cDbyGV6JjyIqyMV7pPRvW6WqBAJV4G1KvnNREQQC8Vuu+Xxw8CDk5pqHgSAkEIl+aQQKRSZpjRZ0AAcOupHTaEGvwMditvAJG/iCTRQS2aRXo9nCAeaylnf5hg3ss1wAdeIgi/RGCzqYWjTdyGnzgg6Qlt1EQQfjhokj6GVr97PlBw+xc+R0tpz1AGWr91oe1+x8+KH5JNGjB1x6KexNkh1CiyMz9VZIMZU8xQKq8OMniBOFAwfTGEefUAPsD1nPUrYdKablxslguvJDRiW0CYZgn8NzN5Bx7nicuhI3lfhIJ0Aa5W99RecLEldzp14eeMAkQIXjcpmEqAH21lSE1ovM1Nsgn/At5VQfiTMPoPER4G1WAXCQMpawNaI6oo8A37Ff6pgnkYppP8Wji3CHwlrdVOKhmKprbm05I/x++N//td5+1VUtZ4eQNETUWyHfsd8ykryQcsqpZjPWteiNsFuX5BWan26HPsUR9ZdzoOlW9Dk62EKfiOfPh2CcevzLl7eMDUJSEVFvhcTz5WtMIlQarohytzU4UKTFWVAVmh9/nFwGP+koRwu5xPLy4u+TRdt2gYh6K2QMR8XEyztQDCQPDy6G0M1yJu9AMTwUpy60PHuGX4cvquG2jzR2H3NNyxkxejTEKzVxxRUtZ4eQNETUWyET6M8Quh2Zlbtx0o1sLmAEYKJRrmAMabjw4CItFIt+ASPoiLee0YXmotcnf6Kg8yn4yKCSHKrxcqDTSfT85K8ta8gnn8Rmvw4dCjNntqwdQlKQ6JdWzCHK2EsxHcmgBx1iolr8BNjCQYJo+pHbpBBMIXEcfGst5Z+tw3vKUHIvTlKd+WDQiPiGDTB1KkyYkBw7hITT7krvFlHBZgpCIX7d6qzFEo+DlLGVg2TgZhBdY9LtK6lmAZsopZph9GRQArsJ1aDR7KaIPRTRkQz6k2fpRxdq2bcGdnxlaqQPmgLOVupCLvtmHwf+9h7K5STvF+eRMSAq5j0QMAue338PI0fCuHHNUztm40Yzq+/c2ZQGzohaE6ishHffNYlUp54KQ2IL5TT5nmttyhkvX27q65x1VmwpBiGC+kQ9paZ2n7ORL9iECs1p57CGyxlDf5uNITSa91nLCnYACgfGTz2N8fQMpcuvYRdvsPLIa9awiy5kcjMTbTXBsIOfAC+zhF0UotE4UHjxcD0nkh3lsxUgGIDXr4KNc4xGOFzg9sL1n0OudSmepLF12pP0eul2euBEo3A8dTM7fvY8ff52qTlg7144+WTYv9+EIToccPzx8P77saLbWLSG//5vM5N3OIyIOp3w0UfmXAArVsCZZ5rs2EDAvOaaa+Cf/wSlEnPPKyvNw2TRIvPJwuWCLl3giy+gZ8/EXGs7JGV86js5zJdsxk8QHwGqCeAjwGyWxnS7j8d37GMlO/ETxB8aoxI/r7AEjSZIkDfDBL2GA5QxP7Jta5NYwCZ2chgfAfwEqSZAERWW5xZg+UwjLr5yUzO9ugTK9sO/L0m2ZZEUfryZXi/dHopfLyONUtxU0P3v11K2dr856PrrYds2KCkxtdnLymDxYvj97xNnyNtvw7PPGlEtLzfnKiyE884z4hoMwvnnw6FDZl95ubFl1iz4z3+ABN3zBx+Er74y11hRYc61fbt5eAiNJmVEfRU78ccR73hx3dEsY7vlA8CHn10UsoZdcSuRr2SnXVPrpebBEo4GtnGozdUybwmWPWHEJQINhzfD4S1JMcmSww/9C2Xx/tIoCh58w4jn/PmxddsrK40IJ4onnzRCGk3NA2T5clPfxmr/E08ACbrnzzwT2XQEzKeCzz83Ai80ipRxv/iiOhaFY9X4wopoIa1BoY7MmOMRtHkOO9Rlb9BGg4v2RqDKertyxN+XFKqqcFi8hxRBqKwyghaP6vq7ZtkmWkiPGKKgKmSHI858L/TahNzzuq7JqiGJYIuUmakfS8+YBU0wImjXpz6cXpZjaKA3HRlB77ivTeRi6VC6Wy6K5pEd0VhaMAy7ElwWSw3pnRrQBKMFyJp+If44ayKdbjvPlOodMSJ2UdTtNr1PE8W0aZAZpz/A+PFwwgnWC7Ne75Ha8Qm555dcYp0Qdcwxkc28hQaRMqI+kDwGh0WqOFC4cHAOx8QtSxvNcfSiFx3xhMZwhsa4mBG4QmV4JzIw5nVpuDiP4Qm7ltMYQg7pR+yoiVe/KBSnLkQy/mfQ5WjwhHJuXOngzoRLXm5dDYfyph3P7pE3Uo2XIIogDnxksPOM++gwqb856LnnTBXImkXRzEyzaPjQQ4kzZNo0GDu2NknJ4zHne+EFSEszv7/0khHxmnj3rCyziHr99UCC7vkf/gC9e9fakZFhrv2FFxJ3re2QlAppNOVoD7KBvaThYji96WKj21A4QTSb2M8mCvDiYSS9YxJ6tnOIT9hAOdUMoTsTGRSTAdpUfARYxx52cphcMhlO70aX8W0PBHzw7Ruw9VPI6QMjr4PsHsm2ypp9T35NxeOzweki886ryLtyVOQBhw4ZYduwwYjvFVckLvKlhkAA5s41X3l5cN11JqQwnG3b4PnnYd8+OPtsE6kSFm6YkHteUQGzZ8PChTB4sFkkzc1t2rWlOO0uTj0RBAlykDLScTc6hNBPgEOUk4knbr/QnRymAh8D6JKwcEihDaA1bNpkBLJ//2Y7zYEvCyhevp+eVwwkPS9+z9k6+fBD83C57rr45QeEFqVdxakngrXs5j2+IUCQAJredGQqo+MKsxXL2MaHfAtoAmgGkMfFjDySCLWTw7zIoohIm0kMZiKDEn05Qmtj8WK4/HITh6419O0Lr70GxyauRWDp5lL2HHsN/areIxsP/DcsH/UQo5fHLwG8efNmHn74YWbNmkVpaSlZGRlMq6jgLq1NV9vbbjOz9fffT5idQvMg08MwdlPE26yiAh/VBAgQZAeHeYUltsfYRAHzWE81/iNjbKaAN1gBmE8Bz/F1TOjkp3zH9zZDL4U2yqFDJqFn69ba2O8NG0y2ZkVFwk6ze9i15FfNxUUVaZSQRgnDVvyc1Ve9a3n83LlzGT58ODNnzqSkpAStNSXl5czUmuHA3JoDP/jA9GAVWjUi6mEsYktMWGMQTQGlFGAvbvZLNsUIthH2A5RRxVK2xw1L/CiBCUxCK+Tll2ND9bQ2YYRvvpmQUxxafJD+le8eadRRg4dyOs3+Y8zxmzdvZurUqZSXl+Pz+SL2+YByYCqwuWbjww8nxE6h+RBRD6OQ8rglbUuwF3xbgnUMsBNFKVUcxiLpI0SpzXMIbZRdu6xn5NXVsGdPQk5RvKKAQJyw16zArphtDz/8cIyYR+MD/l7zS5W8R1s7IuphDCDPMorFT5Du5NgaI58ucQtv5ZLJULrHfW1f4nSnF1KDk06yXmx0uxNWRbHnpf3RFu/hAE72ZE+K2T5r1ixbov5izS+9pF5/a0dEPYwT6Es67ghRduNkAv1shxOewkA8uCJk3Y2TMzgaF06OIpdcYhM/HCjOpgWbEwstz+TJZkE0PDzR64WJE03STwLwdPawbuxfqA4Lww3goppsOjwd27u0tLTU1rhHjpo9OwFWCs2JhDRGUUYVX7KZ79hHBh4m0J+hdI+pZV4XRVSwgE1s4QDZpHMSAyIyToMEeZdvWMMugmh60oEfMkoaXLQHKirgkUdM/LfLBTfcAD/5ScJbza257gNyZv2R7MAO9uRMouPMX9Hr0vyY43JyciixUWclRymKPv4YJk1KqJ1Cw5E4dUEQ4nLLLbcwc+bMOl0wbreb6dOnM2PGjBa0TIhHfaJer/tFKZWulFqslFqllFqrlPqdxTFKKfWIUmqTUmq1Ump0Uw23YjU7mcEnPMhcnmIBWzgQsb+ICl5lGX/kff7CPD5ifdzKjc3JanbxJz7gft7l97zLqyyNKPjlJ8B8vuWvzOMh3mc2yygksuTdVg4yky94kLk8yiesSmAVyIaw+iV4dDA84IUnx8CWTxr2+mAQXrsC7nfB7xQ81AFWPh95zIFv4aUp8GAmPK7KzKQAACAASURBVNwDFjwIwQbWc1o1C/7Y0ZzjfhfMnmrOXYO/CubfC3/JgwezTInYwm2RYyz8f8aG3yn4vRveualhNiSMM880ufZKmcJat9wSsbtoO7x6KTyUDX/uAh/+Avzh6/PBoMlCdbnMGB06mE8G4Xz7LUyZwl3PPou7Hp+62+3mZz/7WcOvo6oK7r3XZKxmZZlaL9uibvpnn5msWa8XBg6EF1+0Hqu5eeklk9Hq9cKYMaZ5SDjbt8Oll5r6PF26wC9+Eb8wWpKpd6aulFJApta6VCnlBr4AbtdaLww7ZgpwGzAFGAf8P631uLrGbehMfSnb+JD1EeGCLhxcxVjyyaUSHzP4lAqqj0SwuHBwFJ2ZRp2mJJRv2ctslsVs70VHfsxJALzMYrZyMCJ8MgM3P2USGXjYziFeYhG+sP3GLz+EsfRr/osIsfQJmHdnZIlVVwb8aC7kn2pvjGcnwvYFsdsvfRWOmQrFO+GxYVBVDDV/OJcXjp0KFz0f+zorvn0L/n1R7PZe4+GGr83PL59rHkj+muATB3g7w083QEZnWPIYvGeRmzP0ErjsNXt2JISxY00noGh++lN49FEqC+HRIVBxAHTo7eFKh6NOgavnhY6dOBEWWNz0V181re127oRhw6C4GLRmLiZs0acUvjA9cLvduN1uXnvtNSZPntzwazn3XCOONRE/DofpsrRhg/n+xRcmoak87A3m9cIf/2iSnVqKJ56AO++MtCMjw5RQOPVUU2t+yBA4cKB2ppCeDqecAvPmWY/ZjDR5pq4NNesk7tBX9JPgQuCF0LELgY5KqYRV3tBoPmFDTPy3n+CR5hQr2EE1/gjD/KHkob0UJ8qUenmftZbbd1FIMZUUUBIj6GBqvSxnOwDz+TZC0Gv2f8p3LVZ6Vwfh4/tia2b7K2D+L+2NUX7AWtAB3r/dfF/4j5DQhl2Wvxy+mQ3FsRF4lsyN8/9/10Io3g0F66IEHSAI1WWm2QPAR/dYj7H+P+BPYNXbOqmuthZ0gMceA2DFM1BdWivoYGbpO76EvaswwmMl6AC3h276P/5hhDYk4JOB1cB0h4Oc7GwcDgc5OTlMnz6d1atXN07Q162LFHQwglhWVtsA+557IoUUzO+/+U3dZYgTSTAI990Xa0dFBfwy9EZ/5hkoLY386FdZCV9+CatWtYydDcBW9ItSyqmUWgnsBz7UWi+KOqQXsCPs952hbQmhEh/VcZpDHAity++m0LIeugL2t6Co1xVrvpPD7KfEMuTRT5BdFAJQgHVEgp8gFbSMwlQWmm42VhSsszfGjoXx95WFGv3sXAgBi0typdk/T+ne+Pt2fm36aDosCmL4K8z5If61ouHQRnt2NJk1a+LvCwnKrsXmoReNcsD+NZjCWPHYH7rpCxfG1DIfAMzIyqLo9dcJBAIUFRUxY8YMBgwY0LBrqGHNGuP+iaaiotbGtdYTICoqTF/UlqCwMH5DjnWhN+DixbGiD+aTR11/syRhS9S11gGt9UigNzBWKRXdIt0qNCRmSqmUmq6UWqqUWlpQYD8lPg0XTos65wAdMeFheWRbxphroLNFCGFzkV5HvfMedKATmZZzbScOupINQCesK/I5UHWOn0jScsAZp9xNx3x7Y/QYWcf4puUrXY8DZfF/P1AFnW3qSUYdpbd7jIbOAyNntjU408z5wbiV4tGxpTxeFo2djxCqZ9t1mHUdc63NdTKyjpveIXTTjzvOWnCrqqCxIh7NwIGRM9sa0tLM+SF+MTOXq+XqqefkGJusqKlaOWyYcbdEo7W5zlZGg+LUtdaFwKfAOVG7dgJ9wn7vDey2eP2TWusxWusxeXl5DTDSwYn0j2lg4cbJJMx/hNEchTPqcpwoupBJLzraPldTOR3r/5i5ZNIJLz3IoSvZOKOeg04cHE9fACYxJOYB5cbJePrFXGNz4XDBST83zYQj7PDCaX+wN0ZOb+gSJ/T+9AfM9wl3mll5OK506Hc6dLJZwPD0B6235w6BTv2MsHcdBs6oVAOnB8aEFkNP+rn1GEdNBE9LRZpmZcUXuktNY+rRN1pfR5ejodc4TH3yY+Lc9AdCN/3OO2OFLD0dTj89cVUjR482YuiJMtbjgZtCN/3++40PPRyv19iX4BDPuLhc8POfW9vxh9Ab/cYbra/j6KNhXMut19nFTvRLnlKqY+jnDOBMiClS8jZwTSgKZjxQpLVOTN5ziFMYyCkMJA0XCkUWaZzHcQyhGwBZpHEdE+hFRxRmVns03ZnG+AbFmDeV0RzFJAZHuFh60IEbORkwrfF+xFiG0gNnyLKedOA6xh8p8zuIrlzACLJJR6FIw8VJDOBU7LZpTwyn3AcTfwVpHUE5IasnnP8kDDnf/hg3LQuJTQiHC079ba2Y5g4yC3xdjzPncKXD8KvNQqpdRv/YPGjCXSw9xsD05eZnpWDaBzB0qhFA5YSeJ8D1n0N2qGn9pN/A2NuMG6OGfmfAtfPt25EQvvsudvZ3xhnw738DkNUNrv/C3FPlBIcbjr4Yrv4wrDnFsmWRYuNywW9/WyumgwaZBb7jjjPlf9PT4eqrzUJqolDKFACbOtUIoNNpOip9/rlp+gEwZQo8/bTJUnU6zaz5nnuMT70lue8++NWvoGNHY0fPnqaP6/mhN3q3bmZRd9w4s7+mE9WH4Te99WAn+mU48DzgxDwEZmut71dK3QygtX48FCEzAzODLweu11rXGdrS2Dh1jcZPEBeOuGLtJ4ADlfQa5eVUk44rrh1BggTRuOK4luxca0uggyYk0JXe+Pdw0A+VxSbiJB7+SiNSDuvbYYvyQ5CeY+1Dr7Ej6Ld2YYDxGFQW1j1Gi1BdDQUF0L17RGOKcPxV5l7FtdPvNxEuneu46ZWVRqTinCMh+P3my8qFAcaNUVlpPj3E643aEgSDxgWVXscbvarK3Csr91UL0a6Sj/wEWM0uvmE3abg4nr4MxL6bR2ge9q6EhX+Hw1thwFlwwi0mjLCGmiiUb9+ErK5mxnzUyYm3Y/OHsPSfUFUEx1wGI6+NFPfSfbD4Udj2OXQZCuPvgLyhtfu1hg1vGVsD1TDiGhh2RZSobttmokuWLTMuiDvuiOgoFAzA2n/DqufNWsLoH5uZdkMelv4qWP0ifPMKeHJgzM0w8OxG35bWjdbw1lsmYqa62nRGqonBb6e0G1EPEOR5vmYfJUdCH904GUc+p3N0ws4jNIz1b8Ab08wsXAeNiKZ3gptWGFdCdSk8dYJJBPJXAArcGXDmn2Fs/J4ODebT38FXf64N0XR7Ie9Y+K8vjEumcCs8ebx5wASqQq6gNLhyDvQ7zbxmzk0mGcsXKrTpzoS+E+GqOSG3zerVcPLJZtbp85kZcFqacTmMGoXW8K8LYcvHkWMcexlc+Iy96wj44LlTYd+qsGvJhHG3wxkPJOputSJuuskkBpWFblhmponDnzMnubP6JNLkOPW2wnr2Rgg6mNjur9lCcZxyuELzEgzAnOlGfGqiT/yVJn79i1Af5WVPhQk6gDbHf/g/UGWvhH29lO6FL/4YGXPvKzchk2tD9ak+use4XQKhiFQdMMe8c4OZLBasg1Uv1ooxmJ+3fW5EGjAJQiUlRtDBfC8thVvN02nrp5GCXjPG2n/DvtX2rmX96+bYiGspg68fth/X32ZYt85kmJaF3bCyMvOQ/Pjj+K9r56SMqH/HvpjkJDARMNtooZhXIYJDm8Bn8TwN+uC7d8zPG96MSgoK4XSbmOxEsG2BGS8aX5nJRgX4/kPrsMfinVBxEL6fj0WQrhljU02Ht6+/tjZg0SLQmu8/jBT0GoL+0Pg22PCO9RhOD2z7zN4YbYb5848kSEVQViZt9eogZUQ9E0+cpcSWi+0WIknvYATcihqfurer9f5gINLv3hTixbErp3EBQW3cvBVurxnDakHSmQbeLqFfMuPkQ3i9oBQZudax/w533bH24WTmGbujUcq4tVKKTp2sfedpaab+imBJyoj6KIs4dTD1X/ojb4BkkNUd+pxoRCscdyaMv9P8PO622Fh45YCcXtC9jjyahpA/CTwWeuv0wPHTQ3b8d6wdTg8cfZHZfvRFkeGONTicMHxa6Jfp0yNrpYOJpLjhBgCOuyqOIDtg6A/tXcvoG2Pj+sE8LPqfaW+MNsNFF1n7zZ1OmDYtdrsApJCodyWb8xmOGydpuPDgJJt0rmZciyXsCLFM/Td0H2GEMa2DWSg94VYTNQJmofGMB01GZ1oHI/idBsKP3k9cCLDDBVd/BDl9wJNtsmXdXjjvn9BtuDlm3G1w3DRjX1oHY0+fk+D8p8x+T5axyduldoy0HJg62yRZASZZ5dxzjZB36GC+T55sClQB2T3g8tfN+DWvz+hiCqSl2WusRddjTa6AO9O8xpMN2b3gmvnWLqY2TVaWcbN06WKqI+bkmK/Zs02SlWBJykS/1OAjwA4O48EZSkRqfckB7ZH930DJbpPd6bX44FRVDDsXgTcXuo9qnpwOHYRdS0zETe/x1rP3kj2mhkrHfMi1yPUK+k29mKAfek+wnjWzbZspbTtkSEQ4Yw2BatjxlXnY9B7fuHh4Xzns+NpcQ6+x1p8iUga/39SL8ftN2794af3thPqiX1Iu2NONU9wtrQxfuakgWLLbzICPOjlWtL97B9a8YnzGk34HHY6K3F9VDOteN4uW+adBz+MbbodyQO86srqDfhP/vfVTU2Jg0u9MElI4ZfvNtQR9xsboUgbBAGxa05eCdX3pUgWDeseKtnPh5+Q/8o9QNucvTP3uMALVsOFtOPy9cUH1PzNWtN1e6H9Gw64/mj3LTTRORmdTYji9jnWFpOJymVDReAQCpkzuunUmdX/KlOaJY1+/3pzH6zW14RtQ6qQlSbmZutC62L8WnptohMpfaXy/vSfAj941Puug3zThKNwS+bop/2eSlMDMSGedbWbagWrjZhh8AVzyUuJmqKX74ZF+kaGCygHXfVabCLXyBXj3JkAZW5SCU38DJ4cqtJYfgGdOMrN9f4V5gGV2hR9/bR5WAJx3Hrz7buTJr7sOnn0WMA0wnp5gwjn9FcYdlDvY2OGx6FndGHQQ3rzWlBUO+EKlE5RxAzVH0lezcuCAaei9Z4+p7piRAV27mkikRIru3Xeb8seBgHlgaG1KN5zfgJoZCaLdxKkLrZNXL4WKw8blEfSbcLydX8GiR83+D38eK+gA7/3UPASCAdMAo7rEvDboM8L73Tvwzb8SZ+cr58fWjtdB01wDTLbpuzcbm/wVJp7dXwmf3W9cSwDv3wGHtxhbg37zvWgbzP1pzUW9FyvoAM89B8tNoZo3rzPnOjJGqXkwfhLbM7rRrHvdJIX5ykP3s8yc518XN7zjVNK54w7YssXkB/j95vu2bSZnIFF8/jk8/rh5aFRXmzK8FRVw5ZUmD6GVIaIuNBuF20ymZnR8t68cVjxtfl71QpwXa1jxLOxeCj6LOHZfWe0YAJs3b+aWW24hJyfnSJOHW265hc2bN9uydU+cD41VxSZWfcPb1n7+QDV8Y2ptsf712BDOoN+UP9Aa+Otf4xvw5z9TXQbbvzCJTxHnqILVs2xdhi1WPG0d6x6oMusabYrXX69N9qrB74c337SOcW8ML75oXU/d6TRFy1oZIupCs2GVzBOzr47/dzoQOi7OomkwJH5z585l+PDhzJw5k5KSErTWlJSUMHPmTIYPH87cuXPrt7WOfcFAHdeia0U4noYcea1VffEaAoG6jUiglzTetShV99+sVRLvptd1rxtKIGB9Hq0Te54EIaIuNBsd8028eTSuDBhxrfn52MvjvFjByP+CXidYh+q5M2Hk9WaGPnXqVMrLy/FFzdh8Ph/l5eVMnTq13hl7TWij1Xk69jXlhq0Ez5UOx5hS5ww5P7bhh3LCoPNCs/y6+m7efTeeLOtIFqcHjr2iTvMbxIhrzXVFoxwmGqdNcf75sYuiTqdZu0hUCNVVV1knlvn98IMfJOYcCUREXWg2lDJx6jVx4WAW+7oNNxUQAc7+B2R2i33tGQ+a5hQOF1w624hQTUVFd6ZpfD38R/Dwww/HiHk0Pp+Pv//973Uec+Vbsc0nULV13bN7wg/+Zh5IDneo4FeGKaTVY5Q5ZvKjkNOzdkHTk2USsKbMCI13ySWmWXE0P/zhkfrnFz0HGbngDhujU384/fd1mt8ghl1hImfcmeYaXenm7zN1dhuMdX/0UVP/PCt0w7KyTLniGTPqfl1DOOMMI+xer0mG8njMguzTT9d2k2pFSPSL0OxUFppFzeJd0GcCDDwncjYaDMKS/4N1r5oY9kn3Q7eohollBabUbPkB0xWp76nmoZGTk0NJvB6TYeTk5FBUVFTnMf5K+OQ3JoY8dyCc8ZAR5XAObTZ2BqpNydxux0WNUWWiSgrWmvK9x1xiUbv9rbfgkUfMjPJ//gfOOitid3WZKTRWE9I45ILEi63Wpin49x+Zh8iwK2pLJrQ5qqrgP/8xPU+HDjUPz3i125vCkiVmoTsrCy6/HPr0qf81zUC7Kb0rWFO23xTW6jQguf9pC7eaUL+uwyAtu3FjLPgjHFgHE//XiC6Aw+HAznvY4XDg9wc4sN5EenQfaTEzTxD13vNAAFauNKI+YkSr7J4jtF7aXfKRYAj64Z2bYM3LJusxUGX81+c/1bIfsSsL4d+XmDBGp8fERU/8FZxyr/0x1vwL/nNl7e+rX4QO+XDHFsjKyrI1U8/0ZvHYMSYO3OECFFzwtJlJJwpb9/yzz+Cyy0xInNamhdqbb8LxjcimEgQLxKeeonx2P6z9FwQqTacff6X5SJ/IeGc7vH4V7PjCnL+q2MR4L3jQuCjsEi7oNRRthdmXwrRp03DX06TY7XYzXF/NgQ0mnLKq2NyTN66BgvUNu566qPee799vasPs32/iqUtLYedO47Mts4gxFIRGIKKeoiyeEZtM46+ApY+1nA1l+00aeqA6cruvDL78i70x5t8Xf9+3/4G77rqrXlF3Od2M52cxYYGBalj6uD077FDvPX/lFeN6iSYQgDfeSJwhQrtGRD1FqSqOs70kcTkZ9VFxKL6rp2yfvTHqmknrIAwYMIDXXnsNr9cbI+5utxuv18vf7niNXMeA2Nf7oSSB3YLqved795pWd9FUV5vZuyAkABH1FKXXCdbbexzfcutynQfG1lIH49MeYLNR8in3xN+XFmoKMXnyZFavXs306dMjMkqnT5/O6tWruerWyQQsoh7dmTDoXHt22KHee37aabWhd+G4XHDqqYkzRGjXiKinKJMfNaJV05RBOc3vUx5tORscLnM+t5cjWaEOD6R3NIulduh1QvzuSFe9XfvzgAEDmDFjBkVFRQQCAYqKipgxYwYDBgwgp7dpYh2ecOPKgE794DgLf31jqfeen3kmjB1r4p1ryMw0fnZZKBUShIQ0pjAHNsCXf4I9K0yjipN/CV2Obnk7dnxlfOhF20zSy4S7YuO/62PWObB5HqBNk4kr3ob8ifZfr7WpwbJkBlQWw7DLYcxPrGuqN4V673l1NTzzDDz/vJmh33CD6eLjtGiJJAgWSJy6IAhCCiGld4Vm5eBGePEsuN8FD3jhnekmuach7FkBT58E9zvhoRyY9z+RETOBavjwF/DHDuaYmeNh97LEXgdgSuMefbRJBe/e3WR9JmnSI7QyNm40mb8ul3GfTZ/eKsvugszUhSZQfhAeHWQSjGrCBZ1p0HMM/NcX9sY4vAUeHx75IHBlwKApcNlr5vfXroQNb5nwwBrcWXDzCrMYmxDmzzfFoSrCTuL1wq9/Db/8ZYJOIrRJDh6EQYOgsLD2IZ+WZjpWfWHzjZ5AZKYuNBvLZ5oEm/D470AV7F1pfya98G+mXko4/grY+K7J/izeBd++ESnoYM771cNNMj+S++6LFHQwNbQffNBU4xPaLzNnmlDU8AlwVZUp9bCsOT4yNg0RdaHR7F0RK7ZgwvcO2MzU3LMitrEEmBn/gQ1waKNFQSxMjPneFQ2zt06++856u88Hhw4l8ERCm2PFitgHPpg3+voEpiQnCBF1odH0GG1cJdFobSoU2h3DKpY9UAVdhkDnQaFPA1E4XNB9VMPsrZMhQ6y3ezzQuXMCTyS0OUaPNqV2o9HaVIVsZYioC41m1I/BnUFEZyJnmqkv3tNm2PWEO03xq3BcGaaxRIejTJONoT+MfXi40uHEu5tkfiQPPBD7H9frhXvvbZ7O9ELb4cc/Nu+N8Ky9tDQYNapV5heIqAuNxpsLP14I/c80M2d3Joy8znSlt0vHfLh+AfQ5ySTrpOXA2J/CJS/VHnPR86YZRVoHc0zvE+G6z6BzbOZ/4zn9dNPvMjz65U9/gp//PIEnEdokubmwcKFJHnO5TMLYddeBjTaJyUCiXwRBENoQTY5+UUr1UUp9opRar5Raq5S63eKYSUqpIqXUytBXCxd4bVtobUrPPn86PDkGvvyz6XbToDGCsOoFeOZkeGosLP6/2GqILYGvHL76q7mO506Dta/GhnZv/xJeuRAeHwnz7jbNMsKpLIRPfwtPjIYXz4aNrXMCZIuNc+GRQfBABjwyADbMSZIhX34JF14II0fC3XfDnj31v0ZICeqdqSulegA9tNbLlVLZwDLgIq31urBjJgF3a63Ps3vi9jxTn3cXLH3ClKCFUB2S/jB9qXWkhxWvXwUb3q4dw+01haOu/QQcLZRx7q+CmePg4He1UTDuTBj1XzD5EfP76lkw56bakrROD3hy4OaVxl9eWQRPjITSvbULou5MmHgfnFxHMa/WyMrn4a3rYref/xSMvqEFDZk1C266yYRkglnszckxIXi9LDqBC22KJs/UtdZ7tNbLQz+XAOsBeWc0kuKdZlbtC5uZ+ytMu7c1L9sbY+8qk4wTPoav3IT4bXo/oebWydp/m7Zt4WGNvjJY9pS5noAP5t4WWWM8UA1VhfB5qJHy0scjBb1mjM/uDyU1tSHeu9V6+9yYz7bNiM8Ht91WK+hg6s0UFsLvE9i9Wmi1NGihVCmVD4wCFlnsnqCUWqWUmquUOjYBtqUkO76y7o3pK4ON79kbY9tnELTotVBdClvmN82+hrDp/cgHSw1Ot3G5HN5sWrxFE/SHinNhkoysQhadabC7jX2Qs7oXAP5y01y7Rdi82TpZyu+HefNayAghmdgWdaVUFvA6cIfWOrodwHKgr9Z6BPAo8GacMaYrpZYqpZYWFBQ01uY2TWacMrLKBdk2P/9kdrV+MDjTIbtn421rKDm9rGPMwdiYkYtlHXOobcic3YuIkMgagv7496rVUkedekdLxZnl5prZuhXdkth5XGgxbL3VlFJujKC/pLWO6S6ptS7WWpeGfn4PcCululgc96TWeozWekxeXl4TTW+b9J0I6Z2IEQCXB8bcbG+MIReGmidH4XDC8GlNNtE2o6db2KEgLRv6nQ6ZeeZ79API7YUTQ5GC428PxbqHD+E0awxdj2s205uFo06x3t57QgsakZdnwjM9UTfd65XwzHaCnegXBTwNrNda/y3OMd1Dx6GUGhsa92AiDU0VlAOu/djU2HZ7zaJhWke4+EXIs5mc5s4wY3ToC54s8GSDNw+ufKfhdcqbQu4gmPov85DyZJvryR0cuVh7yctG7FwZJs7clWEaZAy92OzvPR4mzzCvT8sx+7uPNLHuLdWhKVFc/YG5/nA6DYCrP2phQ15+GU45xSTMdOhgvv/qV3DxxS1siJAM7ES/nAwsANYANZ7Be4GjALTWjyulfgr8BPADFcCdWuuv6hq3PUe/gAn7O/Ct8YN3Hxm/l2d9Y+xfY1wV3Ua0XNRLNAGfKeLlyTTlAazEuHAblO6BvGPNTD4af6VZAM7obB4WbZmC9bA9lFDVNZmrS9u2mVDGY4+FbIubLrRJpElGK6VoB3zzL6guMWVme41rezPTGlY8CyufM6I+8dfQpyXdDYLQzqhP1KWoRRJY+yq8ea1JIApUw9cPwzGXwYXPtC1hDwbhsWPg4IbabZvmwgm3wJT/S55dgtCekdovLUxViUlQ8VeYSoRoE8e97tWWjTFPBF/9OVLQa1jymKmFLghCyyOi3sJsmW/CF6PxlcGal2K3t2ZWPB1/38J/tJwdgiDUIqLewihn/HDmZC10NhZVh71WIZeCIDQ/IuotTP8zjC89GncmjLi25e1pCifESYsHUyddEISWR0S9hXF7Yeps892dadLhXRkw+kbIPy3Z1jWMcbdBzxNit0/8dcvGywuCUIt8SE4CgybDHdtN+d3qUhh4jv3Eo9bGjYtNzZplT5pEqIm/Nm3oBEFIDu1S1PdRTAlV9CCHTNLqf0Ez4M2F42+s+5iS3bBvNXTs17qFMn+SKQXgzmz7iUOJoHCrSSzLHQKd+iXbGqG90a5EvYwqXmYJByjFgSJAkBPI50yORtVVjamF0UGY8xPTBMOVZmLZe42FK982qfStidWzYM7NZpFXa2Pfj96DbsOTbVnLE6g2de43vmvcaoEqGHAOTH3Ffp18QWgq7cqn/jrL2UcxPgJU4cdPkKVs4xt2J9u0CBY9CmtmQaASqopMTPvOhfB2SzZasMH+tfDOTSYcs6rYZMeW7IIXzoxfnTGV+fhXxhXlr/m7VcLmD+CjXybbMqE90W5EvZRKdlBIkMiyCD4CLGRLkqyyZtEjkY0lwMz6NrwVuz2ZLH8qlEAVhb8Kvm/pIlatgGVPRjYMAfN7XfH8gpBo2o2oV+LHEcfFUknrmlZWFcXf15pEvawAtEWzDjRUHm5xc5JOvCYZvvLYvq2C0Fy0G1HvTCZui8t1oBhM6+rG0P8sU6I3mpzepvFEa2HI+WZxNJqgD/qe2vL2JJveJ8bZPr5t1fQR2jbtRtQdKM5jOO6w+boLB148nMzApNoWzZkPmRrlzlBgjnKauPbzZ7YucRh6CXQfESns7kyYcLfpitTemBKqC1/TFMThNmGek2ck1y6hfdHuSu/upZhFbKGQcvrThTH0JQOLnLvFkgAAB6VJREFU3nBJpnQfLJ5h6nJ3GQLjf2Yaa7Q2AtWw6kVTRjgt23RvGvCDZFuVPIq2m7o3e5ZB99Gms1PH/GRbJaQSUk9dEMKoLodgNaR3bPwYgWrjI3clJ8VBaOdIPXVBwHReem5ibUlgtxcueAaGXW5/jNK9Jqx08wdG1PucBBc+DZ1bl/dOaOe0G5+60H4JBuGxYyNrvPvK4fUrYM9ym2ME4JmTYNMHpn2gDsCOL+DpCaZGviC0FkTUhZRnzcvxww3f/5m9MTZ/EArh9Ndu00HwVcA3rzTdRkFIFCLqQsqze0n8fYc22hvj4EbjS4/GV2YaTQtCa0FEXUh5+p8Rf1/3UfbG6D4CnO7Y7Z4s6Hl84+wShOZARF1IeYZcAF6r/DIFkx+xN0bfUyH36NrcATDdnTK6wDFTE2KmICQEEXWhXXDbxlDGZyh5K7sXXL8AOg+w93ql4LpPTLnkjM6mGuVxP4IbF0kFRqF1IXHqgiAIbYj64tRlpi4IgpBCiKgLgiCkECLqgiAIKYSIuiAIQgohoi4IgpBCiKgLgiCkECLqgiAIKYSIuiAIQgpRr6grpfoopT5RSq1XSq1VSt1ucYxSSj2ilNqklFqtlBrdPOa2H3wVsPI5mHMTfPVXKD+QbIsEQWgL2GmS4Qfu0lovV0plA8uUUh9qrdeFHTMZGBT6Ggf8M/RdaATlB+CpsVC231QBdGXA57+H6z43haUEQRDiUe9MXWu9R2u9PPRzCbAeiG4rfCHwgjYsBDoqpXok3Np2wse/huKdtTXA/RVQVQxvXptcuwRBaP00yKeulMoHRgGLonb1AnaE/b6TWOEXbLL+dQj6YrcXrIOKwy1vjyAIbQfboq6UygJeB+7QWhdH77Z4SUylMKXUdKXUUqXU0oKCgoZZ2o5weuLvc0hXWUEQ6sCWqCul3BhBf0lr/R+LQ3YCfcJ+7w3sjj5Ia/2k1nqM1npMXl5eY+xtF4z6sfGjh6Oc0HcipGUnxyZBENoGdqJfFPA0sF5r/bc4h70NXBOKghkPFGmt9yTQznbFKfdAnxPBnWnE3ZMNHfvCxS8k2zJBEFo7dj7MnwRcDaxRSq0MbbsXOApAa/048B4wBdgElAPXJ97U9oMrHa75CHYtgb0roGM+9DsDHM5kWyYIQmunXlHXWn+Btc88/BgN3JooowRDrxPMlyAIgl0ko1QQBCGFEFEXBEFIIUTUBUEQUggRdUEQhBRCRF0QBCGFUCZwJQknVqoA2JaUkxu6AG2l9mFbsVXsTCxtxU5oO7amgp19tdZxszeTJurJRim1VGs9Jtl22KGt2Cp2Jpa2Yie0HVvbg53ifhEEQUghRNQFQRBSiPYs6k8m24AG0FZsFTsTS1uxE9qOrSlvZ7v1qQuCIKQi7XmmLgiCkHK0C1FXSjmVUiuUUnMs9k1SShUppVaGvv43STZuVUqtCdmw1GJ/q2nubcPW1nJPOyqlXlNKfRtqnD4han+ruKc27Gwt93NImA0rlVLFSqk7oo5J+j21aWdruac/U0qtVUp9o5R6RSmVHrW/4fdTa53yX8CdwMvAHIt9k6y2J8HGrUCXOvZPAeZiKmaOBxa1Yltbyz19Hrgh9LMH6Nga76kNO1vF/YyyyQnsxcRMt7p7asPOpN9TTMvPLUBG6PfZwHVNvZ8pP1NXSvUGzgVmJtuWJiLNvRuAUioHmIhp8ILWulprXfj/2zt31ijCKAw/L0TBBAtBUMRCbSwsxBTBC1gYFQKSyiKCjY0XxFb0P1gIFoIINt7AYKxUUtinSEQQrbwQ4y3BRhQLg6/FTsL6uZudRdj5GM/T7Ow3Z+HlYTi78y3DScoqd1oyZ44MA69spw8QVu40oV3OXOgD1kjqA/r5e2Jc1z5r39SBy8B54NcKNXskPZP0SNKOHuVKMTApaVrSyRbncxru3SkrVO90G7AA3Ci23q5LGkhqcnBaJidU7zNlDLjTYj0Hp820ywkVO7X9HrgEzAIfaUyMm0zKuvZZ66Yu6Qgwb3t6hbIZGrdmO4ErwIOehPubfbYHgRHgrKT9yflSw717RKesOTjtAwaBq7Z3Ad+BC0lNDk7L5MzB5zKSVgOjwL1Wp1usVXKddshZuVNJ62j8Et8KbAIGJB1Py1p8dEWftW7qNEbxjUp6C9wFDki62Vxg+6vtb8XxQ2CVpPW9Dmr7Q/E6D0wAQ0lJqeHevaBT1kyczgFztqeK9+M0mmdaU7XTjjkz8dnMCDBj+3OLczk4XaJtzkycHgTe2F6w/RO4D+xNarr2Weumbvui7c22t9C4DXti+49vQkkbJak4HqLh5Esvc0oakLR26Rg4DDxPyrIY7l0maw5ObX8C3knaXiwNAy+SssqdlsmZg8+EY7Tf0qjcaRNtc2bidBbYLam/yDIMvExquvZZZvB07ZB0GpaHZh8FzkhaBH4AYy7+du4hG4CJ4hrrA27bfpzkzGW4d5msOTgFOAfcKm7DXwMnMnXaKWcuPpHUDxwCTjWtZee0RM7KndqekjROYytoEXgKXPtXn/FEaRAEQY2o9fZLEATB/0Y09SAIghoRTT0IgqBGRFMPgiCoEdHUgyAIakQ09SAIghoRTT0IgqBGRFMPgiCoEb8BqCCdo1sbq70AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pring the data points with prediced labels\n",
    "plt.scatter(features[:,0], features[:,1], c= km_model.labels_, cmap='rainbow' )\n",
    "\n",
    "#print the predicted centroids\n",
    "plt.scatter(km_model.cluster_centers_[:, 0], km_model.cluster_centers_[:, 1], s=100, c='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x215aa38e790>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# converting categorical labels to numbers\n",
    "\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "labels = le.fit_transform(labels)\n",
    "\n",
    "#pring the data points with original labels\n",
    "plt.scatter(features[:,0], features[:,1], c= labels, cmap='rainbow' )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
